Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

ABSTRACT: Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationshi

SUBMITTER: Maglanoc LA 

PROVIDER: S-EPMC7267936 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature